Ten weirdest computers

Today's computers use pulses of electricity and flipping magnets to manipulate and store data. But information can be processed in many other, weirder, ways .

1. Optical computing

There's nothing weird about encoding data in light - global communications depend on optical fibre. But using light signals to actually process data and carry out computations is still not practical.

Optical computers are a worthwhile goal because using light could increase a computer's speed and the quantity of data it can handle. But trapping, storing and manipulating light is difficult.

Research by people like Paul Braun, at the University of Illinois, Urbana Champaign, US, is bringing us closer to this goal. He has created 3D optical waveguides out of photonic crystals that should make possible to trap light, slow it down and bend it around sharp corners, without fear of it escaping.

Meanwhile Mikhail Lukin at Harvard University has developed what is essentially an optical version of the transistor that underlies all today's computing power. Lukin and colleagues have created a way to make a single photon from one light signal switch another light signal on and off.

2. Quantum computing

If you want to tear up all the rules of classical computing, look no further than quantum computers. Instead of using electronic bits of information that exist in either 1 or 0 states, they use quantum mechanical effects to create qubits that can be in both states at once.

Calculations show that this ability allows many parallel computations to be carried out. As the number of qubits a quantum computer increases, the data it can process increases exponentially.

That would make possible things that are unfeasible with today's computers - such as rapidly factoring extremely large numbers to crack cryptographic keys.

3. DNA computing

DNA may be the perfect material for carrying out computations. In a sense that is precisely what it evolved to do: DNA processes data and runs programs stored in sequences of genomic base pairs, as well as coordinating proteins that process information themselves to keep organisms alive.

The first person to co-opt these processes for computational problems was Leonard Adleman at the University of Southern California. In 1994, he used DNA to solve a well-known mathematical problem called the 7-point Hamiltonian Path problem.

The basic principle is to use sequences of DNA to recognise shorter "input" strands, and to produce different "output" sequences. The results can then be read, for example, through the activation of fluorescent proteins.

Recently DNA-computing enthusiasts have become interested in having their creations go to work inside biological systems like the human body. It makes sense, because that's where they fit in best - and where conventional computers fit in least.

4. Reversible computing

Some people think we should be recycling our bits as well as our trash.

Hardware companies have long tried to reduce the power consumption of computers. One unusual way to do this is by engineering chips that are "reversible".

Normally every computational operation that involves losing a bit of information also discards the energy used to represent it. Reversible computing aims to recover and reuse this energy.

One way to do this, which is being developed by Michael Frank at the University of Florida, US, involves making versions of logic gates than can run in reverse.

Every computing operation involves feeding inputs into logic gates, which produce output signals. Instead of discarding the energy of those signals, Frank's gates run in reverse after every operation. That returns the energy of the output signal to the start of the circuit where it is used to carry a new input signal.

It may sound odd, but according to Frank, as computing power improves it won't be long before chips' wastefulness will be a major limit to their performance.

5. Billiard Ball computing

Computing today involves chain reactions of electrons passing from molecule to molecule inside a circuit. So it makes sense to try and harness other kinds of chain reaction for computing - even dominoes or marbles.

6. Neuronal computing

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